Graphical Abstract:

Abstract:

Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity
and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of
leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently,
the development of new treatments for leishmaniasis is a priority in the field of neglected
tropical diseases. The aim of this work is to develop computational models those allow the identification
of new chemical compounds with potential anti-leishmanial activity. A data set of 116 organic chemicals,
assayed against promastigotes of Leishmania amazonensis, is used to develop the theoretical models.
The cutoff value to consider a compound as active one was IC50≤1.5μM. For this study, we employed
Dragon software to calculate the molecular descriptors and WEKA to obtain machine learning
(ML) models. All ML models showed accuracy values between 82% and 91%, for the training set. The
models developed with k-nearest neighbors and classification trees showed sensitivity values of 97%
and 100%, respectively; while the models developed with artificial neural networks and support vector
machine showed specificity values of 94% and 92%, respectively. In order to validate our models, an
external test-set was evaluated with good behavior for all models. A virtual screening was performed
and 156 compounds were identified as potential anti-leishmanial by all the ML models. This investigation
highlights the merits of ML-based techniques as an alternative to other more traditional methods to
find new chemical compounds with anti-leishmanial activity.

Abstract:Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity
and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of
leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently,
the development of new treatments for leishmaniasis is a priority in the field of neglected
tropical diseases. The aim of this work is to develop computational models those allow the identification
of new chemical compounds with potential anti-leishmanial activity. A data set of 116 organic chemicals,
assayed against promastigotes of Leishmania amazonensis, is used to develop the theoretical models.
The cutoff value to consider a compound as active one was IC50≤1.5μM. For this study, we employed
Dragon software to calculate the molecular descriptors and WEKA to obtain machine learning
(ML) models. All ML models showed accuracy values between 82% and 91%, for the training set. The
models developed with k-nearest neighbors and classification trees showed sensitivity values of 97%
and 100%, respectively; while the models developed with artificial neural networks and support vector
machine showed specificity values of 94% and 92%, respectively. In order to validate our models, an
external test-set was evaluated with good behavior for all models. A virtual screening was performed
and 156 compounds were identified as potential anti-leishmanial by all the ML models. This investigation
highlights the merits of ML-based techniques as an alternative to other more traditional methods to
find new chemical compounds with anti-leishmanial activity.